Summary of Scribeagent: Towards Specialized Web Agents Using Production-scale Workflow Data, by Junhong Shen et al.
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data
by Junhong Shen, Atishay Jain, Zedian Xiao, Ishan Amlekar, Mouad Hadji, Aaron Podolny, Ameet Talwalkar
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes an alternative approach to improving Large Language Model (LLM) agents for complex web-based tasks. Rather than relying on general-purpose models like GPT-4 and designing better prompts, the authors fine-tune open-source LLMs using production-scale workflow data collected from over 250 domains corresponding to 6 billion tokens. This approach shows substantial gains over prompting-based agents on existing benchmarks, achieving state-of-the-art direct generation performance on Mind2Web and improving the task success rate by 7.3% on WebArena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about training computer models to do a better job at understanding websites and planning what to do next. Right now, most of these models are not very good at this because they don’t understand specific things like HTML code. The researchers tried a new way to make the models better by teaching them with real data from over 250 different types of workflows. This approach works really well and is even better than other methods that try to improve the models’ understanding. |
Keywords
» Artificial intelligence » Gpt » Large language model » Prompting
